neMEMSi: One step forward in wireless attitude and heading reference systems

Comotti, D.; Galizzi, Michael; Vitali, Andrea

doi:10.1109/isiss.2014.6782521

Cited by 19 publications

(13 citation statements)

References 1 publication

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“…The presented platform, called neMEMSi-TEG, aims to provide a wireless AHRS with an ultra-low power consumption, preserving at the same time the accuracy of power greedy state-of-the-art solutions [2].…”

Section: System Architecturementioning

confidence: 99%

“…Power consumption of inertial sensors is strongly affected by the presence of the gyroscope, which could draws up to few mA [2]. In order to keep the power consumption as low as possible, a gyroscope has not been integrated on the neMEMSi-TEG platform.…”

Section: Ahrs Performancementioning

confidence: 99%

“…Moreover, the same orientation has been compared to the one computed by a full 9 Degrees-of-Freedom (DOF) AHRS [2]. Figures 5, 6 and 7 depict the roll, pitch and yaw Euler angles achieved with the proposed algorithm, the 9 DOF AHRS and the reference encoder for three different rotations over the x, y and z axes respectively.…”

Section: Ahrs Performancementioning

confidence: 99%

See 2 more Smart Citations

An inertial and environmental wireless platform with advanced energy harvesting capabilities

Galizzi

Comotti

Gasparini

et al. 2015

2015 IEEE International Symposium on Inertial Sensors and Systems (ISISS) Proceedings

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This work presents neMEMSi-TEG, an ultra-low power MEMS based inertial and magnetic platform with embedded processing and wireless communication capabilities. The platform embeds an advanced energy management IC that allows an extremely high efficiency energy harvesting suitable for low-power and miniaturized Thermoelectric Generators. This paper aims to provide a description of the architecture of the system, the embedded orientation estimation algorithm and the performance in terms of power consumption and orientation accuracy.

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Section: System Architecturementioning

confidence: 99%

Section: Ahrs Performancementioning

confidence: 99%

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An inertial and environmental wireless platform with advanced energy harvesting capabilities

Galizzi

Comotti

Gasparini

et al. 2015

2015 IEEE International Symposium on Inertial Sensors and Systems (ISISS) Proceedings

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“…The final system will make real time calculations and signal processing directly on board, thanks to the integrated microcontroller which has been specifically designed for advanced and low-power embedded computations. In fact, the board used so far in this project is a prototype board called neMEMSi [29,30], designed for processing signals in real-time, eventually saving and transmitting them. The processing unit of neMEMSi is the STM32L1, an ultralow-power 32 bit microcontroller provided by STMicroelectronics, with the outstanding 33.3 DMIPS peak computation capability and an extremely low power consumption scalable down to 233uA/MHz.…”

Section: Introductionmentioning

confidence: 99%

“…Further details about the hardware description can be found in Ref. [29]. Respect to the other systems proposed in literature, SHESS has major energetic and computational benefits.…”

Section: Introductionmentioning

confidence: 99%

Smart sensors for the recognition of specific human motion disorders in Parkinson's disease

Lorenzi

Rao

Romano

et al. 2015

2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI)

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It is proposed a wearable sensing system based on Inertial Measurement Units (IMUs) for the long-time detection of specific human motion disorders. The system uses a single sensor positioned on the head, close to the ear. The system recognizes noticeable gait features as irregular steps and the gait block (freezing of gait). Respect to other positions on the body, the headset has the maximum sensitivity to the trunk oscillations which patients make to get out of the block, increasing dramatically the risk of falls. The headset has also the advantage that it is easy to wear and the whole system can be contained in a single package. In fact, an audio device for auditory feedback to the patient can be integrated without any wireless/wired connection to the ear. The classification of those motion features is performed by an artificial neural network (ANN) and starts from the raw signals collected by the IMU. The ANN algorithm of recognition is extremely versatile and works for any individual gait features. The ANN allows robust and reliable detection of the targeted kinetic features and requires fast and light calculations.In this paper, it is presented the recognition of irregular steps, trunk oscillations and stop state obtained performing calculations out-board on a PC, without losing the generality of the method validity. The final headset system will be extremely energy efficient thanks to its compactness, to the fact that the ANN avoids computational energy wasting, and that the audio feedback does not require any wired/wireless connection. This affects positively the system performance in terms of power consumption and battery life (monitoring time).

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Using Neural Networks for the Recognition of Specific Motion Symptoms of the Parkinson’s Disease

Lorenzi

Rao

Romano

et al. 2016

Advances in Neural Networks

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neMEMSi: One step forward in wireless attitude and heading reference systems

Cited by 19 publications

References 1 publication

An inertial and environmental wireless platform with advanced energy harvesting capabilities

An inertial and environmental wireless platform with advanced energy harvesting capabilities

Smart sensors for the recognition of specific human motion disorders in Parkinson's disease

Using Neural Networks for the Recognition of Specific Motion Symptoms of the Parkinson’s Disease

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